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Popular Data Scientist title, mid-tier services brand, and likely metro location increase candidate competition.
Strong ML/MLOps skills transferable, but client-facing analytics experience favors similar industries.
Explicit 7-9 years requirement, senior ML/MLOps leadership and professional certification make shortlisting stringent.
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Lead client engagement for analytics projects, managing problem framing, success criteria, and executive communication.
Own delivery management across multiple workstreams, ensuring on-time and on-budget completion with benefit tracking.
Lead end-to-end AI/ML model development and deployment, including design of predictive, prescriptive, and generative AI solutions and driving ML best practices adoption.
7-9 years of professional experience.
Professional certification such as CA, CPA, CISA, or CIA.
Experience in AI/ML model development, deployment, and production-grade ML pipelines using MLOps principles.
Not explicitly mentioned: explicit degree requirements, location constraints, or notice period.
Experienced in managing analytics programs at the client and executive level with strong governance skills.
Skilled in translating business goals into actionable data science roadmaps with measurable outcomes.
Technically proficient in full AI/ML lifecycle including feature engineering, model explainability, bias detection, and monitoring in production environments.